Using Simulations to Investigate the Longitudinal Stability of Alternative Schemes for Classifying and Identifying Children with Reading Disabilities

Autor/inn/en	Schatschneider, Christopher; Wagner, Richard K.; Hart, Sara A.; Tighe, Elizabeth L.
Titel	Using Simulations to Investigate the Longitudinal Stability of Alternative Schemes for Classifying and Identifying Children with Reading Disabilities
Quelle	In: Scientific Studies of Reading, 20 (2016) 1, S.34-48 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1088-8438
DOI	10.1080/10888438.2015.1107072
Schlagwörter	Reading Difficulties; Learning Disabilities; At Risk Students; Disability Identification; Simulation; Classification; Low Achievement; Criteria; Models; Grade 1; Elementary School Students; Reading Tests; Verbal Ability; Intelligence Tests; Vocabulary; Emergent Literacy; Reading Fluency; Monte Carlo Methods; Florida; Dynamic Indicators of Basic Early Literacy Skills (DIBELS); Peabody Picture Vocabulary Test + Suchen Sie Ihr Suchwort? Reading difficulty; Leseschwierigkeit; Learning handicap; Lernbehinderung; Simulation program; Simulationsprogramm; Classification system; Klassifikation; Klassifikationssystem; Unterdurchschnittliche Leistung; Analogiemodell; School year 01; 1. Schuljahr; Schuljahr 01; Lesetest; Mündliche Leistung; Intelligence test; Intelligenztest; Wortschatz; Frühleseunterricht; Monte-Carlo-Methode
Abstract	The present study employed data simulation techniques to investigate the 1-year stability of alternative classification schemes for identifying children with reading disabilities. Classification schemes investigated include low performance, unexpected low performance, dual-discrepancy, and a rudimentary form of constellation model of reading disabilities that included multiple criteria. Data from a previously published study were used to construct a growth model of reading development. The parameters estimated from this model were then used to construct three simulated data sets wherein the growth parameters were manipulated in one of three ways: a stable-growth pattern, a mastery learning pattern, and a fan-spread pattern. Results indicated that overall the constellation model provided the most stable classifications across all conditions of the simulation, and that classification schemes were most stable in the fan-spread condition and were the least stable under the mastery learning growth pattern. These results also demonstrate the utility of data simulations in reading research. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01